SOTAVerified

Word Similarity

Calculate a numerical score for the semantic similarity between two words.

Papers

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Showing 201–250 of 378 papers

TitleStatusHype
Evaluation of taxonomic and neural embedding methods for calculating semantic similarity—0
Evaluation of word embeddings against cognitive processes: primed reaction times in lexical decision and naming tasks—0
EVALution-MAN: A Chinese Dataset for the Training and Evaluation of DSMs—0
Expansional Retrofitting for Word Vector Enrichment—0
Explicit Retrofitting of Distributional Word Vectors—0
Exploring Transformers for Ranking Portuguese Semantic Relations—0
FBK: Machine Translation Evaluation and Word Similarity metrics for Semantic Textual Similarity—0
Features of word similarity—0
Ferryman at SemEval-2020 Task 3: Bert with TFIDF-Weighting for Predicting the Effect of Context in Word Similarity—0
Fine-tuning Distributional Semantic Models for Closely-Related Languages—0
Gaussian Word Embedding with a Wasserstein Distance Loss—0
Generating Varied Training Corpora in Runyankore Using a Combined Semantic and Syntactic, Pattern-Grammar-based Approach—0
GNEG: Graph-Based Negative Sampling for word2vec—0
hauWE: Hausa Words Embedding for Natural Language Processing—0
HCCL at SemEval-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic Similarity—0
HG2Vec: Improved Word Embeddings from Dictionary and Thesaurus Based Heterogeneous Graph—0
HHU at SemEval-2017 Task 2: Fast Hash-Based Embeddings for Semantic Word Similarity Assessment—0
HindiWSD: A package for word sense disambiguation in Hinglish & Hindi—0
Hitachi at SemEval-2020 Task 3: Exploring the Representation Spaces of Transformers for Human Sense Word Similarity—0
Hybed: Hyperbolic Neural Graph Embedding—0
Identity-sensitive Word Embedding through Heterogeneous Networks—0
Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search—0
Implanting Rational Knowledge into Distributed Representation at Morpheme Level—0
Implicitly Incorporating Morphological Information into Word Embedding—0
Improved Word Embeddings with Implicit Structure Information—0
Improve Lexicon-based Word Embeddings By Word Sense Disambiguation—0
Improving Contextual Representation with Gloss Regularized Pre-training—0
Improving Contextual Representation with Gloss Regularized Pre-training—0
Improving Distributional Similarity with Lessons Learned from Word Embeddings—0
Improving Multilingual Neural Machine Translation For Low-Resource Languages: French,English - Vietnamese—0
Improving Multilingual Neural Machine Translation For Low-Resource Languages: French, English - Vietnamese—0
Improving sparse word similarity models with asymmetric measures—0
Improving Word Alignment using Word Similarity—0
Improving Word Embeddings through Iterative Refinement of Word- and Character-level Models—0
Improving Word Embeddings Using Kernel PCA—0
Injecting Word Embeddings with Another Language's Resource : An Application of Bilingual Embeddings—0
Integrating Distributional Lexical Contrast into Word Embeddings for Antonym-Synonym Distinction—0
Interpretable probabilistic embeddings: bridging the gap between topic models and neural networks—0
Investigating Cross-Lingual Alignment Methods for Contextualized Embeddings with Token-Level Evaluation—0
Joint Embeddings of Chinese Words, Characters, and Fine-grained Subcharacter Components—0
Joint Training for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora—0
Joint Unsupervised Learning of Semantic Representation of Words and Roles in Dependency Trees—0
JUSTMasters at SemEval-2020 Task 3: Multilingual Deep Learning Model to Predict the Effect of Context in Word Similarity—0
KNET: A General Framework for Learning Word Embedding using Morphological Knowledge—0
k-Rater Reliability: The Correct Unit of Reliability for Aggregated Human Annotations—0
L2F/INESC-ID at SemEval-2017 Tasks 1 and 2: Lexical and semantic features in word and textual similarity—0
Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction—0
Latent Semantic Analysis Models on Wikipedia and TASA—0
BURT: BERT-inspired Universal Representation from Twin Structure—0
Learning Bilingual Sentiment-Specific Word Embeddings without Cross-lingual Supervision—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Context-to-VectorSpearman's Rho78.9—Unverified
2Bert2VecSpearman's Rho72.8—Unverified
3SkipGramSpearman's Rho61—Unverified